University of Nevada, Reno

About: University of Nevada, Reno is a education organization based out in Reno, Nevada, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 13561 authors who have published 28217 publications receiving 882002 citations. The organization is also known as: University of Nevada & Nevada State University.

Characterization of a Murine Monoclonal Antibody to Cryptococcus neoformans Polysaccharide That Is a Candidate for Human Therapeutic Studies

Abstract: The murine monoclonal antibody (MAb) 18B7 [immunoglobulin G1(κ)] is in preclinical development for treatment of Cryptococcus neoformans infections. In anticipation of its use in humans, we defined the serological and biological properties of MAb 18B7 in detail. Structural comparison to the related protective MAb 2H1 revealed conservation of the antigen binding site despite several amino acid differences. MAb 18B7 was shown by immunofluorescence and agglutination studies to bind to all four serotypes of C. neoformans, opsonize C. neoformans serotypes A and D, enhance human and mouse effector cell antifungal activity, and activate the complement pathway leading to deposition of complement component 3 (C3) on the cryptococcal capsule. Administration of MAb 18B7 to mice led to rapid clearance of serum cryptococcal antigen and deposition in the liver and spleen. Immunohistochemical studies revealed that MAb 18B7 bound to capsular glucuronoxylomannan in infected mouse tissues. No reactivity of MAb 18B7 with normal human, rat, or mouse tissues was detected. The results show that both the variable and constant regions of MAb 18B7 are biologically functional and support the use of this MAb in human therapeutic trials.

Monocular precrash vehicle detection: features and classifiers

Abstract: Robust and reliable vehicle detection from images acquired by a moving vehicle (i.e., on-road vehicle detection) is an important problem with applications to driver assistance systems and autonomous, self-guided vehicles. The focus of this work is on the issues of feature extraction and classification for rear-view vehicle detection. Specifically, by treating the problem of vehicle detection as a two-class classification problem, we have investigated several different feature extraction methods such as principal component analysis, wavelets, and Gabor filters. To evaluate the extracted features, we have experimented with two popular classifiers, neural networks and support vector machines (SVMs). Based on our evaluation results, we have developed an on-board real-time monocular vehicle detection system that is capable of acquiring grey-scale images, using Ford's proprietary low-light camera, achieving an average detection rate of 10 Hz. Our vehicle detection algorithm consists of two main steps: a multiscale driven hypothesis generation step and an appearance-based hypothesis verification step. During the hypothesis generation step, image locations where vehicles might be present are extracted. This step uses multiscale techniques not only to speed up detection, but also to improve system robustness. The appearance-based hypothesis verification step verifies the hypotheses using Gabor features and SVMs. The system has been tested in Ford's concept vehicle under different traffic conditions (e.g., structured highway, complex urban streets, and varying weather conditions), illustrating good performance.

Hybridization and the evolution of invasiveness in plants and other organisms

Abstract: Less than a decade ago, we proposed that hybridization could serve as a stimulus for the evolution of invasiveness in plants (Ellstrand and Schierenbeck Proc Nat Acad Sci USA 97:7043–7050, 2000). A substantial amount of research has taken place on that topic since the publication of that paper, stimulating the symposium that makes up this special issue. Here we present an update of this emergent field, based both on the papers in this volume and on the relevant literature. We reevaluate the lists that we presented in our earlier paper of reports in which hybridization has preceded the evolution of invasiveness. We discard a few cases that were found to be in error, published only as abstracts, or based on personal communication. Then we augment the list from examples in this volume and a supplementary literature search. Despite the omissions, the total number of cases has increased. Many have been strengthened. We add a list of cases in which there has been evidence that intra-taxon hybridization has preceded the evolution of invasiveness. We also provide a number of examples from organisms other than plants. We consider how our examples suggest mechanisms whereby hybridization may act to stimulate the evolution of invasiveness. Hybridization does not represent the only evolutionary pathway to invasiveness, but it is one that can explain why the appearance of invasiveness often involves a long lag time and/or multiple introductions of exotics.

The returns management process

Abstract: Returns management is the supply chain management process by which activities associated with returns, reverse logistics, gatekeeping, and avoidance are managed within the firm and across key members of the supply chain. The correct implementation of this process enables management not only to manage the reverse product flow efficiently, but to identify opportunities to reduce unwanted returns and to control reusable assets such as containers. In this paper, we describe how the returns management process can be implemented within a firm and across the supply chain. The process is described in terms of its sub‐processes and associated activities, and the interfaces with corporate functions, other supply chain management processes and other firms. Examples of successful implementation are provided.

Probabilistic Seismic Hazard Analysis without the Ergodic Assumption

Abstract: INTRODUCTION An ergodic process is a random process in which the distribution of a random variable in space is the same as the distribution of that same random variable at a single point when sampled as a function of time. An ergodic assumption is commonly made in probabilistic seismic hazard analysis (PSHA). A regression analysis is used to obtain a mean curve to predict ground motion as a function of magnitude and distance (and sometimes other parameters). The standard deviation of this ground-motion regression is determined mainly by the misfit between observations and the corresponding predicted ground motions at multiple stations for a small number of well recorded earthquakes. Thus, the standard deviation of the ground-motion regression is dominantly related to the statistics of the spatial variability of the ground motions. An ergodic assumption is made when PSHA treats that spatial uncertainty of ground motions as an uncertainty over time...